ml-experiment-skills

Generates structured, rigorous experiment plans for machine learning research. Use this skill whenever a user wants to design, plan, or structure an ML experiment — including baseline comparisons, ablation studies, hyperparameter sweeps, architecture searches, data experiments, fine-tuning runs, or evaluation protocols. Trigger on phrases like "experiment plan", "research plan", "how should I test", "design an experiment", "ablation study", "benchmark this", "evaluate my model", "compare these approaches", or any request to systematically validate an ML idea or hypothesis. Also use for questions like "how do I know if X works better than Y" in an ML context. Produces a research-design plan (hypothesis, conditions, metrics, decision rules) — NOT a code-implementation plan. Implementation planning is a separate follow-up step, ideally run in plan mode after the experiment plan is accepted. Do NOT use for pure theory questions, math derivations, non-ML software engineering tasks, or for planning the code/scripts

Writes a structured report on ML experiment results, grounded in the experiment's DESIGN.md and the actual results files. Use this skill whenever a user wants to document, summarise, or communicate the outcome of a machine learning experiment — including phrases like "write a report", "summarise my results", "document what happened", "write up the experiment", "generate a summary of exp_N", "what did my experiment show", or "I finished running my experiment". Always use this skill when the user has completed (or partially completed) an experiment and wants a written record of findings. The report is written to experiments/exp_N/reports/summary.md. Do NOT use for planning new experiments (use ml-experiment-planner instead) or for pure data analysis without a write-up goal.